Also curious.

I'm a sample of 1 and also relatively inexperienced. But I felt I quickly reached the limits of what was possible when I tried doing sentence classification with OSS sentence embedding models. The issue was with negation. I'd attributed too much magic to embedding models - they don't really understand language.

Not to say there isn't very capable tech out there. Just to add a datapoint that "sentiment analysis"-like approaches in blogs don't always scale to your particular use-case.

Edit: conscious I've drifted from the topic of chatbot type models, but felt relevant somehow.

You can look at https://github.com/neuml/txtai. Biggest thing of 2023 was RAG with models like Mistral.

Got it. Thank you. I thought you had delivered consumer facing applications when i read that comment.

I've delivered consulting projects using RAG and Mistral 7B. Not saying it's easy to get the prompts right but I've been able to build systems that work well. Just trying to counter the myth that paid APIs are the only way to do this.